Accurately predicting future outcomes associated with uncertain situations offers the potential to achieve advantageous results in a number of applications. A variety of individuals and organizations utilize the prediction of future outcomes to provide guidance in the study of regularities that underlie natural and social phenomena. In the physical and biological sciences the discovery of strong laws has enabled the prediction of future scenarios with uncanny accuracy. However, traditional attempts at predicting future outcomes are typically less accurate in other areas such as the social sciences and tend to be adversely impacted by a variety of participant characteristics such as risk tendencies and ability to analyze relevant information.
Analyzing collective input from a variety of individuals typically provides greater accuracy in predicting future outcomes. Relying on a single individual to predict a future outcome is usually very precarious. Collective input enables the abilities of a variety of individuals to be leverage and detrimental impacts associated with the frailties of any single participant to be mitigated. However, it is very inconvenient and expensive to gather and analyze predictive inputs from large numbers of participants, frequently dispersed across vast geographical areas. Prediction activities such as the dissemination of information relevant to forecasts and collection of future predictions are typically more difficult in large groups. Activities such as controlling information dissemination and gathering predictions from a small group of individuals is relatively inexpensive and easy. Nevertheless, the collective predictive accuracy of small groups is susceptible to a variety of potential adverse characteristics that impact the collection and analysis of information related to an uncertain situation.
The availability and analysis of information related to an uncertain situation typically has a significant impact on the accuracy of a future outcome prediction. The greater availability of information related to the uncertain situation, the more accurate predictions tend to be. In the business arena, economists have long articulated the belief that markets efficiently collect and disseminate information. In particular, rational expectations theory indicates that markets have the capacity to aggregate information held by individuals and also to convey expectations associated with the information via the price and volume of assets. Therefore, a market where the asset is information rather than a physical good has the potential to provide some guidance on the prediction of future outcomes.
Information markets generally involve the trading of state-contingent securities. If these markets are large enough and properly designed, they can provide more accurate than other techniques for extracting diffuse information, such as surveys and opinions polls. However, information markets tend to suffer from a variety of problems such as information traps, illiquidity, manipulation, and lack of equilibrium. These problems are exacerbated when the groups involved are small (e.g., less than 30 participants) and not very experienced at playing in an information market. Traditional attempts might seem to aggregate dispersed information well, but they are typically very expensive, fragile, context-specific and offer little or no improvement.
To complicate matters further, business and social information relevant to predictions involve people with personal characteristics that tend to skew results, making it hard to identify and accurately aggregate forecasts or predictions. There are a number of characteristics that impact individual reporting, such as risk tendencies and ability to analyze the information. Individuals that are relatively proficient at assimilating and analyzing available information have a tendency to provide better predictions of future outcomes than those that are less proficient at assimilating and analyzing available information. Even when individuals are relatively proficient at assimilating and analyzing available information their personal approach to risk conditions impact their prediction of future outcomes.
Risk attitudes cause most individuals to not necessarily report their true posterior probabilities conditioned solely on the information related to a prediction of an uncertain outcome. In most realistic situations, risk averse persons report a probability distribution that is flatter than their true beliefs as they to spread their bets among all possible outcomes. In the extreme case of risk aversion, individuals report a flat probability distribution regardless of available information. In this case, no predictive information is revealed by the reported prediction. Conversely, risk-loving individuals tend to report a probability distribution that is more sharply peaked around a particular prediction, and in the extreme case of risk loving behavior their optimal response is to put all the weight on the most probable state according to their observations. In this case, their report conveys some, but not all the information contained in their observations.
What is required is a system and method to forecast uncertain events with small groups. The system and method should accurately aggregate information with correct incentives.